Optimum Signal Processing

Digital signal processing is currently in a period of rapid growth caused by recent advances in VLSI technology. This is especially true of three areas of optimum signal processing;namely, real-time adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms.

In this edition the book has been brought up to date by increasing the emphasis on the above areas and including several new developments. The major additions are:a unified presentation of the fast recursive least-squares algorithms for adaptive processing;the discussion of several eigenvector methods of spectrum estimation such as MUSIC, minimum-norm, ESPRIT, spatial smoothing for coherent signals, and others; and discussion of the Schur algorithm for linear prediction admitting efficient parallel implementations,and the more efficient split Schur and split Levinson algorithms. Moreover,older but basic material has been added such as an expanded discussion of Kalman filtering and discussion of classical statistical estimation concepts such as maximum likelihood, Cramer-Rao bound, and asymptotic statistical properties applied to linear prediction and eigenstructure methods.